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SGRU: A High-Performance Structured Gated Recurrent Unit for Traffic Flow Prediction

arXiv 2404.11854 ↗First posted 18 Apr 2024cs.AIcited by 0

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Abstract (arXiv)

Traffic flow prediction is an essential task in constructing smart cities and is a typical Multivariate Time Series (MTS) Problem. Recent research has abandoned Gated Recurrent Units (GRU) and utilized dilated convolutions or temporal slicing for feature extraction, and they have the following drawbacks: (1) Dilated convolutions fail to capture the features of adjacent time steps, resulting in the loss of crucial transitional data. (2) The connections within the same temporal slice are strong, while the connections between different temporal slices are too loose. In light of these limitations, we emphasize the importance of analyzing a complete time series repeatedly and the crucial role of GRU in MTS. Therefore, we propose SGRU: Structured Gated Recurrent Units, which involve structured GRU layers and non-linear units, along with multiple layers of time embedding to enhance the model's fitting performance. We evaluate our approach on four publicly available California traffic datasets: PeMS03, PeMS04, PeMS07, and PeMS08 for regression prediction. Experimental results demonstrate that our model outperforms baseline models with average improvements of 11.7%, 18.6%, 18.5%, and 12.0% respectively.

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References · 17

  1. 2018An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modelingcited by 1,476
  2. 2015Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcastingcited by 1,436
  3. 2017Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecastingcited by 1,136
  4. 2019Graph WaveNet for Deep Spatial-Temporal Graph Modelingcited by 829
  5. 2020Adaptive Graph Convolutional Recurrent Network for Traffic Forecastingcited by 530
  6. 2019GMAN: A Graph Multi-Attention Network for Traffic Predictioncited by 434
  7. 2018T-GCN: A Temporal Graph ConvolutionalNetwork for Traffic Predictioncited by 401
  8. 2021Spectral Temporal Graph Neural Network for Multivariate Time-series Forecastingcited by 299
  9. 2020Spatial-Temporal Fusion Graph Neural Networks for Traffic Flow Forecastingcited by 213
  10. 2018Deep Multi-View Spatial-Temporal Network for Taxi Demand Predictioncited by 162
  11. 2022Spatial-Temporal Identity: A Simple yet Effective Baseline for Multivariate Time Series Forecastingcited by 151
  12. 2021Graph Neural Controlled Differential Equations for Traffic Forecastingcited by 133
5 more
  1. 2022Spatio-Temporal Self-Supervised Learning for Traffic Flow Predictioncited by 77
  2. 2020A3T-GCN: Attention Temporal Graph Convolutional Network for Traffic Forecastingcited by 54
  3. 2021Traffic Flow Forecasting with Spatial-Temporal Graph Diffusion Networkcited by 52
  4. 2022Adaptive Graph Spatial-Temporal Transformer Network for Traffic Flow Forecastingcited by 17
  5. 2021Space Meets Time: Local Spacetime Neural Network For Traffic Flow Forecastingcited by 7

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